Real-World Humanoid RL: LIFT’s Pretraining and On-Device Fine‑Tuning Paradigm

The paper presents LIFT, a framework that combines large‑scale off‑policy SAC pretraining with physics‑informed world‑model fine‑tuning to enable safe, sample‑efficient continual reinforcement learning on real humanoid robots, demonstrating zero‑sample deployment and rapid adaptation across diverse speed distributions.

LIFTSACcontinuous learning

0 likes · 13 min read

Real-World Humanoid RL: LIFT’s Pretraining and On-Device Fine‑Tuning Paradigm

Python Programming Learning Circle

Mar 10, 2023 · Artificial Intelligence

Google's i‑S2R and GoalsEye: Robot Table‑Tennis Learning from Human Interaction

The article explains how Google's i‑S2R and GoalsEye projects use iterative simulation‑to‑real training, behavior cloning and goal‑conditioned learning to enable robots to play table‑tennis with humans, highlighting the challenges, experimental setup, and performance improvements achieved across player skill levels.

AI researchRoboticsbehavior cloning

0 likes · 6 min read

Google's i‑S2R and GoalsEye: Robot Table‑Tennis Learning from Human Interaction